BigW is an Australian Kmart competitor. For this role, Core was contracted in the assistance to help develop the recommendation engine for BigW’s email campaign. Core investigated a wide variety of recommendation engine technologies whose results ultimately decided upon the Machine Learning technology to be deployed by WooliesX & BigW.
During the Covid-19 Crisis, Core created a simple question answering tool in order to help individuals and SMEs cut through the noise to help find what private & government financial stimulus aide they were eligible for.
Core Intelligence is the principal partner to Blink Securetech where Core has assisted in the conception, development & maintenance of the Blink Accounting platform. Blink Accounting is a machine learning powered webapp that provides accountants a high level overview of their clients financial position via various metrics such as Cash Flow. Via ingesting data into Core’s machine learning engines, Blink Accounting is able to serve up various recommendations, such as future cash flow forecasts, to Accountants on how they can improve their client’s overall net position.
Blink Cyber is the other arm of Blink Securetech purely focussing on Cyber Risk & Mitigation. For the Blink Cyber webapp, Core Intelligence designed from first principles a Machine Learning powered recommendation engine. Users answer a questionnaire, whose responses are pushed into the recommendation engine to provide the right tailor made personalised insurance policy for the client.
Qualie is a company that collects both Qualitative – a users opinion – and Quantitative – facts such as numbers survey responses. For this engagement, Core designed a state of the art reinforcement learning algorithm to control how videos were served to Qualie’s users in order to collect data.
Via the use of Core Intelligence’s proprietary question and answer engine, Core was engaged to develop a webapp to display questions & collect responses from Citizens for a new green field residential build.
Core Intelligence was engaged by a major US pet insurance company to deliver a minimum viable product web app design for their product – Petin. The webapp consisted of a questionnaire, collecting user’s responses, displaying information via a dashboard, then use machine learning to create, entirely from scratch, an insurance policy with the right clauses tailored specifically to the user’s answers from the questionnaire.